A Need for Targeted Teaching of Shared Decision-Making as Identified from an Assessment of Client-Centered Communication Skills Training with Companion Animal Veterinarians
Bibliographic record
Abstract
Shared decision-making has been increasingly discussed as a communication practice within veterinary medicine, and it is gaining more traction for diagnostic and treatment planning conversations and specifically offering a spectrum of care. This teaching tip describes the data from an investigation of veterinarians’ shared decision-making in a pre-test/post-test communication skills training intervention that used a client-centered, skills-based communication approach. Practice teams from a purposive sample of four companion animal veterinary clinics in Texas participated in a 15-month communication skills intervention, including interactive group workshops and one-on-one communication coaching. To assess the outcome of the intervention, for nine participating veterinarians, appointments recorded pre- ( n = 85) and post-intervention ( n = 85) were analyzed using the Observer OPTION 5 instrument to assess shared decision-making. The intervention effect was evaluated using mixed logistic regression, adjusting for appointment type. The communication intervention did not significantly impact participating veterinarians’ demonstration of shared decision-making (pre = 25.42, n = 55; post = 28.03, n = 56; p = 0.36). Appointment type was significantly associated with veterinarians’ OPTION 5 scores ( p = .0004) and health problem appointments (OPTION 5 = 30.07) demonstrated greater shared decision-making than preventive care appointments (OPTION 5 = 22.81). Findings suggest that client-centered, skills-based training traditionally used in veterinary curricula and continuing education may not foster the use of shared decision-making, which is a higher-order communication approach that may require a dedicated process-oriented training. This teaching tip highlights the need for a targeted stepwise approach to teach shared decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".